Book Image

Machine Learning for Finance

By : Jannes Klaas
Book Image

Machine Learning for Finance

By: Jannes Klaas

Overview of this book

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways. The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.
Table of Contents (15 chapters)
Machine Learning for Finance
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Legal perspectives

There are two doctrines in anti-discrimination law: disparate treatment, and disparate impact. Let's take a minute to look at each of these:

  • Disparate treatment: This is one kind of unlawful discrimination. Intentionally discriminating against ZIP codes with the hope of discriminating against race is not legal. Disparate treatment problems have less to do with the algorithm and more to do with the organization running it.

  • Disparate impact: This can be a problem if an algorithm is deployed that has a different impact on different groups, even without the organization knowing about it. Let's walk through a lending scenario in which disparate impact could be a problem. Firstly, the plaintiff must establish that there is a disparate impact. Assessing if there's a disparate impact is usually done with the four-fifths rule, which says that if the selection rate of a group is less than 80% of the group, then it is regarded as evidence of adverse impact. If a lender has 150 loan...